OpenRaiser/Intern-Atlas huggingface dataset:
Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards
Venue: arXiv cs.RO 2016
Year: 2016

Ground vehicles equipped with monocular vision systems are a valuable source of high resolution image data for precision agriculture applications in orchards. This paper presents an image processing framework for fruit detection and counting using orchard image data. A general purpose image segmentation approach is used, including two feature learning algorithms; multi-scale Multi-Layered Perceptrons (MLP) and Convolutional Neural Networks (CNN). These networks were extended by including contextual information about how the image data was captured (metadata), which correlates with some of the appearance variations and/or class distributions observed in the data. The pixel-wise fruit segmentation output is processed using the Watershed Segmentation (WS) and Circular Hough Transform (CHT) algorithms to detect and count individual fruits. Experiments were conducted in a commercial apple orchard near Melbourne, Australia. The results show an improvement in fruit segmentation performance with the inclusion of metadata on the previously benchmarked MLP network. We extend this work with CNNs, bringing agrovision closer to the state-of-the-art in computer vision, where although metadata had negligible influence, the best pixel-wise F1-score of $0.791$ was achieved. The WS algorithm produced the best apple detection and counting results, with a detection F1-score of $0.858$. As a final step, image fruit counts were accumulated over multiple rows at the orchard and compared against the post-harvest fruit counts that were obtained from a grading and counting machine. The count estimates using CNN and WS resulted in the best performance for this dataset, with a squared correlation coefficient of $r^2=0.826$.
Learning to Match Using Local and Distributed Representations of Text for Web Search
Venue: arXiv cs.IR 2016
Year: 2016

Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space. In traditional information retrieval models, on the other hand, terms have discrete or local representations, and the relevance of a document is determined by the exact matches of query terms in the body text. We hypothesize that matching with distributed representations complements matching with traditional local representations, and that a combination of the two is favorable. We propose a novel document ranking model composed of two separate deep neural networks, one that matches the query and the document using a local representation, and another that matches the query and the document using learned distributed representations. The two networks are jointly trained as part of a single neural network. We show that this combination or `duet' performs significantly better than either neural network individually on a Web page ranking task, and also significantly outperforms traditional baselines and other recently proposed models based on neural networks.
Location Aggregation of Spatial Population CTMC Models
Venue: arXiv cs.MA 2016
Year: 2016

In this paper we focus on spatial Markov population models, describing the stochastic evolution of populations of agents, explicitly modelling their spatial distribution, representing space as a discrete, finite graph. More specifically, we present a heuristic approach to aggregating spatial locations, which is designed to preserve the dynamical behaviour of the model whilst reducing the computational cost of analysis. Our approach combines stochastic approximation ideas (moment closure, linear noise), with computational statistics (spectral clustering) to obtain an efficient aggregation, which is experimentally shown to be reasonably accurate on two case studies: an instance of epidemic spreading and a London bike sharing scenario.
Modeling Deployment Decisions for Elastic Services with ABS
Venue: arXiv cs.DC 2016
Year: 2016

The use of cloud technology can offer significant savings for the deployment of services, provided that the service is able to make efficient use of the available virtual resources to meet service-level requirements. To avoid software designs that scale poorly, it is important to make deployment decisions for the service at design time, early in the development of the service itself. ABS offers a formal, model-based approach which integrates the design of services with the modeling of deployment decisions. In this paper, we illustrate the main concepts of this approach by modeling a scalable pool of workers with an auto-scaling strategy and by using the model to compare deployment decisions with respect to client traffic with peak loads.
Huddler: Convening Stable and Familiar Crowd Teams Despite Unpredictable Availability
Venue: arXiv cs.HC 2016
Year: 2016

Distributed, parallel crowd workers can accomplish simple tasks through workflows, but teams of collaborating crowd workers are necessary for complex goals. Unfortunately, a fundamental condition for effective teams - familiarity with other members - stands in contrast to crowd work's flexible, on-demand nature. We enable effective crowd teams with Huddler, a system for workers to assemble familiar teams even under unpredictable availability and strict time constraints. Huddler utilizes a dynamic programming algorithm to optimize for highly familiar teammates when individual availability is unknown. We first present a field experiment that demonstrates the value of familiarity for crowd teams: familiar crowd teams doubled the performance of ad-hoc (unfamiliar) teams on a collaborative task. We then report a two-week field deployment wherein Huddler enabled crowd workers to convene highly familiar teams in 18 minutes on average. This research advances the goal of supporting long-term, team-based collaborations without sacrificing the flexibility of crowd work.
A New Piggybacking Design for Systematic MDS Storage Codes
Venue: arXiv cs.IT 2016
Year: 2016

Distributed storage codes have important applications in the design of modern storage systems. In a distributed storage system, every storage node has a probability to fail and once an individual storage node fails, it must be reconstructed using data stored in the surviving nodes. Computation load and network bandwidth are two important issues we need to concern when repairing a failed node. The traditional maximal distance separable (MDS) storage codes have low repair complexity but high repair bandwidth. On the contrary, minimal storage regenerating (MSR) codes have low repair bandwidth but high repair complexity. Fortunately, the newly introduced piggyback codes combine the advantages of both ones. In this paper, by introducing a novel piggybacking design framework for systematic MDS codes, we construct a storage code whose average repair bandwidth rate, i.e., the ratio of average repair bandwidth and the amount of the original data, can be as low as $\frac{\sqrt{2r-1}}{r}$, which significantly improves the ratio $\frac{r-1}{2r-1}$ of the previous result. In the meanwhile, every failed systematic node of the new code can be reconstructed quickly using the decoding algorithm of an MDS code, only with some additional additions over the underlying finite field. This is very fast compared with the complex matrix multiplications needed in the repair of a failed node of an MSR code.
Cube-Split: Structured Quantizers on the Grassmannian of Lines
Venue: arXiv cs.IT 2016
Year: 2016

This paper introduces a new quantization scheme for real and complex Grassmannian sources. The proposed approach relies on a structured codebook based on a geometric construction of a collection of bent grids defined from an initial mesh on the unit-norm sphere. The associated encoding and decoding algorithms have very low complexity (equivalent to a scalar quantizer), while their efficiency (in terms of the achieved distortion) is on par with the best known structured approaches, and compares well with the theoretical bounds. These properties make this codebook suitable for high-resolutions, real-time applications such as channel state feedback in massive multiple-input multiple-output (MIMO) wireless communication systems.
Analysis of Tourism Dynamics and Special Events through Mobile Phone Metadata
Venue: arXiv cs.CY 2016
Year: 2016

Tourism has been an increasingly important factor in global economy, society and environment, accounting for a significant share of GDP and labor force. Policy and research on tourism traditionally rely on surveys and economic datasets, which are based on small samples and depict tourism dynamics at low spatial and temporal granularity. Anonymous call detail records (CDRs) are a novel source of data, showing enormous potential in areas of high societal value: such as epidemics, poverty, and urban development. This study demonstrates the added value of using CDRs for the formulation, analysis and evaluation of tourism strategies, at the national and local levels. In the context of the European country of Andorra, we use CDRs to evaluate marketing strategies in tourism, understand tourists' experiences, and evaluate revenues and externalities generated by touristic events. We do this by extracting novel indicators in high spatial and temporal resolutions, such as tourist flows per country of origin, flows of new tourists, tourist revisits, tourist externalities on transportation congestion, spatial distribution, economic impact, and profiling of tourist interests. We exemplify the use of these indicators for the planning and evaluation of high impact touristic events, such as cultural festivals and sports competitions.
On the Behaviour of Deviant Communities in Online Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

On-line social networks are complex ensembles of inter-linked communities that interact on different topics. Some communities are characterized by what are usually referred to as deviant behaviors, conducts that are commonly considered inappropriate with respect to the society's norms or moral standards. Eating disorders, drug use, and adult content consumption are just a few examples. We refer to such communities as deviant networks. It is commonly believed that such deviant networks are niche, isolated social groups, whose activity is well separated from the mainstream social-media life. According to this assumption, research studies have mostly considered them in isolation. In this work we focused on adult content consumption networks, which are present in many on-line social media and in the Web in general. We found that few small and densely connected communities are responsible for most of the content production. Differently from previous work, we studied how such communities interact with the whole social network. We found that the produced content flows to the rest of the network mostly directly or through bridge-communities, reaching at least 450 times more users. We also show that a large fraction of the users can be inadvertently exposed to such content through indirect content resharing. We also discuss a demographic analysis of the producers and consumers networks. Finally, we show that it is easily possible to identify a few core users to radically uproot the diffusion process. We aim at setting the basis to study deviant communities in context.
Decomposition of Multi-Agent Planning under Distributed Motion and Task LTL Specifications
Venue: arXiv cs.SY 2016
Year: 2016

The aim of this work is to introduce an efficient procedure for discrete multi-agent planning under local complex temporal logic behavior specifications. While the first part of an agent's behavior specification constraints the agent's trace and is independent, the second part of the specification expresses the agent's tasks in terms of the services to be provided along the trace and may impose requests for the other agents' collaborations. To fight the extreme computational complexity of centralized multi-agent planning, we propose a two-phase automata-based solution, where we systematically decouple the planning procedure for the two types of specifications. At first, we only consider the former specifications in a fully decentralized way and we compactly represent each agents' admissible traces by abstracting away the states that are insignificant for the satisfaction of their latter specifications. Second, the synchronized planning procedure uses only the compact representations. The satisfaction of the overall specification is guaranteed by construction for each agent. An illustrative example demonstrating the practical benefits of the solution is included.
Checks and Balances: A Low-complexity High-gain Uplink Power Controller for CoMP
Venue: arXiv cs.SY 2016
Year: 2016

Coordinated Multipoint (CoMP) promised substantial throughput gain for next-generation cellular systems. However, realizing this gain is costly in terms of pilots and backhaul bandwidth, and may require substantial modifications in physicallayer hardware. Targeting efficient throughput gain, we develop a novel coordinated power control scheme for uplink cellular networks called Checks and Balances (C&B), which checks the received signal strength of one user and its generated interference to neighboring base stations, and balances the two. C&B has some highly attractive advantages: C&B (i) can be implemented easily in software, (ii) does not require to upgrade non-CoMP physicallayer hardware, (iii) allows for fully distributed implementation for each user equipment (UE), and (iv) does not need extra pilots or backhaul communications. We evaluate the throughput performance of C&B on an uplink LTE system-level simulation platform, which is carefully calibrated with Huawei. Our simulation results show that C&B achieves much better throughput performance, compared to several widely-used power control schemes.
Optimal model order reduction with the Steiglitz-McBride method for open-loop data
Venue: arXiv cs.SY 2016
Year: 2016

In system identification, it is often difficult to find a physical intuition to choose a noise model structure. The importance of this choice is that, for the prediction error method (PEM) to provide asymptotically efficient estimates, the model orders must be chosen according to the true system. However, if only the plant estimates are of interest and the experiment is performed in open loop, the noise model may be over-parameterized without affecting the asymptotic properties of the plant. The limitation is that, as PEM suffers in general from non-convexity, estimating an unnecessarily large number of parameters will increase the chances of getting trapped in local minima. To avoid this, a high order ARX model can first be estimated by least squares, providing non-parametric estimates of the plant and noise model. Then, model order reduction can be used to obtain a parametric model of the plant only. We review existing methods to perform this, pointing out limitations and connections between them. Then, we propose a method that connects favorable properties from the previously reviewed approaches. We show that the proposed method provides asymptotically efficient estimates of the plant with open loop data. Finally, we perform a simulation study, which suggests that the proposed method is competitive with PEM and other similar methods.
Measuring Fairness in Ranked Outputs
Venue: arXiv cs.DB 2016
Year: 2016

Ranking and scoring are ubiquitous. We consider the setting in which an institution, called a ranker, evaluates a set of individuals based on demographic, behavioral or other characteristics. The final output is a ranking that represents the relative quality of the individuals. While automatic and therefore seemingly objective, rankers can, and often do, discriminate against individuals and systematically disadvantage members of protected groups. This warrants a careful study of the fairness of a ranking scheme. In this paper we propose fairness measures for ranked outputs. We develop a data generation procedure that allows us to systematically control the degree of unfairness in the output, and study the behavior of our measures on these datasets. We then apply our proposed measures to several real datasets, and demonstrate cases of unfairness. Finally, we show preliminary results of incorporating our ranked fairness measures into an optimization framework, and show potential for improving fairness of ranked outputs while maintaining accuracy.
Minimal unsatisfiability and deficiency: recent developments
Venue: arXiv cs.DM 2016
Year: 2016

Starting with Aharoni and Linial in 1986, the deficiency delta(F) = c(F) - n(F) >= 1 for minimally unsatisfiable clause-sets F, the difference of the number of clauses and the number of variables, is playing an important role in investigations into the structure of minimal unsatisfiability. The talk belonging to this extended abstract, available at http://cs.swan.ac.uk/~csoliver/papers.html#BORDEAUX2016 , gives a high-level overview on recent developments.
CogALex-V Shared Task: LexNET - Integrated Path-based and Distributional Method for the Identification of Semantic Relations
Venue: arXiv cs.CL 2016
Year: 2016

We present a submission to the CogALex 2016 shared task on the corpus-based identification of semantic relations, using LexNET (Shwartz and Dagan, 2016), an integrated path-based and distributional method for semantic relation classification. The reported results in the shared task bring this submission to the third place on subtask 1 (word relatedness), and the first place on subtask 2 (semantic relation classification), demonstrating the utility of integrating the complementary path-based and distributional information sources in recognizing concrete semantic relations. Combined with a common similarity measure, LexNET performs fairly good on the word relatedness task (subtask 1). The relatively low performance of LexNET and all other systems on subtask 2, however, confirms the difficulty of the semantic relation classification task, and stresses the need to develop additional methods for this task.
Improving PageRank for Local Community Detection
Venue: arXiv cs.SI 2016
Year: 2016

Community detection is a classical problem in the field of graph mining. While most algorithms work on the entire graph, it is often interesting in practice to recover only the community containing some given set of seed nodes. In this paper, we propose a novel approach to this problem, using some low-dimensional embedding of the graph based on random walks starting from the seed nodes. From this embedding, we propose some simple yet efficient versions of the PageRank algorithm as well as a novel algorithm, called WalkSCAN, that is able to detect multiple communities, possibly overlapping. We provide insights into the performance of these algorithms through the theoretical analysis of a toy network and show that WalkSCAN outperforms existing algorithms on real networks.
Causality and Networks
Venue: arXiv cs.OH 2016
Year: 2016

Causality is omnipresent in scientists' verbalisations of their understanding, even though we have no formal consensual scientific definition for it. In Automata Networks, it suffices to say that automata "influence" one another to introduce a notion of causality. One might argue that this merely is an incidental side effect of preferring statements expressed in natural languages to mathematical formulae. The discussion of this paper shows that if this is the case, then it is worth considering the effects of those preferences on the contents of the statements we make and the formulae we derive. And if it is not the case, that causality is a mere incidental side effect of our preferences of formulation, then causality must be worth some scientific attention per se. In any case, the paper illustrates how the innate sense of causality we have may be made deliberate and formal use of without having to pin down the elusive notion of causality to anything fixed and formal that wouldn't do justice to the wide range of ways it is involved in science-making.
The Composition and Formation of Effective Teams. Computer Science meets Psychology
Venue: arXiv cs.MA 2016
Year: 2016

Nowadays the composition and formation of effective teams is highly important for both companies to assure their competitiveness and for a wide range of emerging applications exploiting multiagent collaboration (e.g. crowdsourcing, human-agent collaborations). The aim of this article is to provide an integrative perspective on team composition, team formation and their relationship with team performance. Thus, we review the contributions in both the computer science literature and the organisational psychology literature dealing with these topics. Our purpose is twofold. First, we aim at identifying the strengths and weaknesses of the contributions made by these two diverse bodies of research. Second, we pursue to identify cross-fertilisation opportunities that help both disciplines benefit from one another. Given the volume of existing literature, our review is not intended to be exhaustive. Instead, we have preferred to focus on the most significant contributions in both fields together with recent contributions that break new ground to spur innovative research.
RSA algorithm with a new approach encryption and decryption message text by ascii
Venue: arXiv cs.CR 2016
Year: 2016

In many research works, there has been an orientation to studying and developing many of the applications of public-key cryptography to secure the data while transmitting in the systems, In this paper we present an approach to encrypt and decrypt the message text according to the ASCII(American Standard Code for Information Interchange) and RSA algorithm by converting the message text into binary representation and dividing this representation to bytes(8s of 0s and 1s) and applying a bijective function between the group of those bytes and the group of characters of ASCII and then using this mechanism to be compatible with using RSA algorithm, finally, Java application was built to apply this approach directly.
Single- and Multi-Task Architectures for Tool Presence Detection Challenge at M2CAI 2016
Venue: arXiv cs.CV 2016
Year: 2016

The tool presence detection challenge at M2CAI 2016 consists of identifying the presence/absence of seven surgical tools in the images of cholecystectomy videos. Here, we propose to use deep architectures that are based on our previous work where we presented several architectures to perform multiple recognition tasks on laparoscopic videos. In this technical report, we present the tool presence detection results using two architectures: (1) a single-task architecture designed to perform solely the tool presence detection task and (2) a multi-task architecture designed to perform jointly phase recognition and tool presence detection. The results show that the multi-task network only slightly improves the tool presence detection results. In constrast, a significant improvement is obtained when there are more data available to train the networks. This significant improvement can be regarded as a call for action for other institutions to start working toward publishing more datasets into the community, so that better models could be generated to perform the task.
Toward Mining Visual Log of Software
Venue: arXiv cs.SE 2016
Year: 2016

In this paper, we define visual log of a software system as data capturing the interactions between its users and its graphic user interface (GUI), such as screen-shots and screen recordings. We vision that mining such visual log could be useful for bug reproducing and debugging, automated GUI testing, user interface designing, question answering of common usages in software support, etc. Toward that vision, we propose a core framework for mining visual log of software. This framework focuses on detecting GUI elements and changes in visual log, removing users' private data, recognizing user interactions with GUI elements, and learning GUI usage patterns. We also performed a small study on the characteristics of GUI elements in mobile apps. The findings from this study suggested several heuristics to design techniques for recognizing GUI elements and interactions.